The process of pasteurization involves heating liquid food products like milk, juices, etc to kill harmful organisms such as viruses, bacteria, molds, and yeast. However, some amount bacteria may survive the pasteurization process or may be inadvertently introduced during further processing. Such bacteria typically cause spoilage of food products and has been estimated to cause economic losses of $1 billion each year. Moreover, if the surviving bacteria are pathogenic, outbreaks of food borne illnesses may occur among consumers who assumed that the food product was risk-free since it had been pasteurized. In the United States alone, it has been estimated that approximately 76 million food borne illnesses occur per year. It has also been estimated that such illnesses result in up to 5000 deaths and have an adverse economic impact of $6.5-$34.9 billion each year.
Detecting and quantifying bacteria that survive treatments such as pasteurization is an important step in assuring food quality and food safety and in complying with standards set by appropriate governing bodies or trade organizations. For instance, the United States Pasteurized Milk Ordinance requires “Grade A” pasteurized milk to have a total bacterial count of ≤20,000 colony forming unit (CFU)/ml and a coliform count of ≤10 CFU/ml. As a consequence, those who produce and/or market food products have to perform microbiological tests to satisfy themselves, and the governing bodies, regarding the efficacy of their processes designed to keep the numbers of bacteria within the stipulated range. It is important to their economic operation that they do so with the least possible expenditure of resources (material and labor).
There are presently several ways to detect bacteria in liquid samples like milk and juice. They can be broadly classified into three broad classes: (a) traditional methods such as plate cultures and biochemical assays, (b) DNA and antibody based methods, often involving micro/nano particles and fluorescence, (c) other “automated” techniques that rely on monitoring the effects of bacterial metabolism on the medium. Of these, traditional methods are the most extensively used, and often serve as the standard to which other techniques are compared. However, such traditional methods are tedious, labor intensive, and require very long times to detect bacteria, which can range from overnight to weeks depending on the type of the organism and medium used.
DNA and antibody based methods overcome many of the disadvantages of the traditional methods. They are rapid, require less reagents and labor, and are able to identify the species/strain of the bacteria present relatively easily. However, DNA and antibody based methods cannot distinguish between viable and dead bacteria, and hence their applicability in many situations (such as that described earlier) is limited.
The commercially available automated methods include devices such as the Bactec™ that detects the amount of radio-labeled carbon dioxide released, Coli-Check™ swabs that use Bromocresol Purple as an indicator to measure the decrease in pH due to bacterial metabolism, and the Bactometer™ (Bactomatic Ltd.), Malthus 2000™ (Malthus Instruments Ltd.) and RABIT™ (Don Whitley Scientific Ltd.) systems, that use electrical impedance. A summary of various automated methods already commercialized, and the times to detection (“TTD”) for these methods (for various mentioned initial loads) are given in Table 1.
TABLE 1Summary of Existing Automated MethodsCommercial nameMethod employedInitial loadMicroorganismsTTDRABIT (DonChange in solution1CFU/mlcoliforms16.1 hrsWhitley ScientificconductanceLtd., Shipley, UK)Bactometer (BioImpedance>105CFU/mlMainly E. coli  4 hoursMerieux,microbiologyNuertingen,Germany)Malthus systemsConductance100CFU/mlC. Sporogenes15.5 hrs(Malthuschange of the fluidInstruments Ltd.,Crawley, UK)BacTrac (Sylab)Impedance100CFU/mlP. Aeruginosa  30 hoursanalyzer
The common underlying feature of these techniques, including those which use electrical impedance, is based on bacterial metabolism to produce a discernable change in a material property of the medium (such as pH, optical density, amount of carbon dioxide dissolved, electrical conductivity). The amount of metabolite processed by an individual bacterium is extremely small. Hence, there has to be a sufficiently large number of bacteria present (either a priori or arising due to proliferation from the smaller number initially present) before the signal generated (change in the material property of the suspension) can be effectively measured. If the bacterial count in the original suspension happens to be small (1000 CFU/ml or lower), one must wait for cells to proliferate to an appropriately high number (often ˜106 CFU/ml or greater) before a discernable change in the physical properties of the medium (such as pH, O2/CO2 concentration, conductivity etc) can be noticed. Thus, for low initial loads, current commercial automated systems take almost as long as the plate-cultures (overnight or longer) to provide the desired result.
Recently, there have been efforts to increase the ease of handling, cut costs, and most importantly, reduce TTDs by using microfluidic systems to miniaturize the automated methods. For example, chip-based micro-devices have been developed in which the pH and impedance of a sample contained therein are monitored in order to detect bacterial metabolism, and various additional modifications like the use of interdigitated microelectrodes, and arrays of microelectrode based biosensors have been tried in order to increase the sensitivity of measurements (with respect to conventional electrodes), and thus further decrease the TTD. While these efforts were successful in the sense that their TTDs are lower than those of the commercially available devices, they continue to be limited by the amount of time it takes for bacterial metabolism to significantly alter the composition of the medium when bacterial loads are low. One method previously attempted to overcome this drawback involved concentrating the bacterial cells from dilute samples to a small volume by using dielectrophoresis (DEP) prior to culture, and then detecting changes in medium composition as before. While the culture time needed for detection was reduced, one needs to take into account the time needed for concentration using DEP (an additional 2-3 hours) as well to get effective TTDs. Again, while successful, the actual method of detection still relies on bacterial metabolism, with its inherent limitations (as discussed earlier).
Therefore, there is a need to provide a new and improved method to detect viable bacteria in a suspension based on the changes of capacitance of the suspension due to the bacteria proliferation. There is another need to provide a new system to detect viable bacteria in a suspension based on the changes of capacitance of the testing suspension.